Although it is well-known that the negative effects of VR sickness, and the desirable sense of presence are important determinants of a user’s immersive VR experience, there remains a lack of definitive research outcomes to enable the creation of methods to predict and/or optimize the trade-offs between them. Most VR sickness assessment (VRSA) and VR presence assessment (VRPA) studies reported to date have utilized simple image patterns as probes, hence their results are difficult to apply to the highly diverse contents encountered in general, real-world VR environments. To help fill this void, we have constructed a large, dedicated VR sickness/presence (VR-SP) database, which contains 100 VR videos with associated human subjective ratings. Using this new resource, we developed a statistical model of spatio-temporal and rotational frame difference maps to predict VR sickness. We also designed an exceptional motion feature, which is expressed as the correlation between an instantaneous change feature and averaged temporal features. By adding additional features (visual activity, content features) to capture the sense of presence, we use the new data resource to explore the relationship between VRSA and VRPA. We also show the aggregate VR-SP model is able to predict VR sickness with an accuracy of 90% and VR presence with an accuracy of 75% using the new VR-SP dataset.

中文翻译：

---

虚拟现实疾病与虚拟现实的存在：一个统计预测模型

尽管众所周知，VR疾病的负面影响以及理想的临场感是决定用户沉浸式VR体验的重要因素，但仍然缺乏确定的研究成果，无法创建预测和/或优化方法他们之间的权衡。迄今为止，大多数报告的VR疾病评估（VRSA）和VR存在评估（VRPA）研究都使用简单的图像模式作为探针，因此其结果难以应用于一般现实世界VR环境中遇到的高度多样化的内容。为了填补这一空白，我们构建了一个大型的专用VR疾病/状态（VR-SP）数据库，其中包含100个带有相关人类主观评分的VR视频。使用这个新资源，我们开发了时空和旋转帧差异图的统计模型来预测VR疾病。我们还设计了一种出色的运动特征，表现为瞬时变化特征和平均时间特征之间的相关性。通过添加其他功能（视觉活动，内容功能）来捕获存在感，我们使用新的数据资源来探索VRSA和VRPA之间的关系。我们还显示，使用新的VR-SP数据集，汇总的VR-SP模型能够以90％的准确性预测VR疾病，以75％的准确性预测VR存在。内容功能）来捕捉存在感，我们使用新的数据资源来探索VRSA和VRPA之间的关系。我们还显示，使用新的VR-SP数据集，汇总的VR-SP模型能够以90％的准确性预测VR疾病，以75％的准确性预测VR存在。内容功能）来捕捉存在感，我们使用新的数据资源来探索VRSA和VRPA之间的关系。我们还显示，使用新的VR-SP数据集，汇总的VR-SP模型能够以90％的准确性预测VR疾病，以75％的准确性预测VR存在。